Getting proper image exposure is a critical component of a digital or cell-phone camera image pipeline. In professional photography, the right exposure is normally decided manually either by visual inspection or by using an external exposure meter. However, in point-and-shoot digital and cell-phone cameras, an auto exposure (AE) module is used to automatically set the exposure level without any user intervention.
A number of AE methods have appeared in the literature [1-5], where the emphasis is placed on choosing the right Exposure Value (EV). EV is related to the aperture f-number F and exposure duration T according to this equation
                    EV        =                              log            2                    (                                    F              2                        T                    )                                    (        1        )            There are normally two different AE modes in cameras called shutter priority and aperture priority which allow setting the exposure based on a fixed shutter speed or lens aperture size, respectively. Most cell-phone cameras use a fixed aperture size lens.
Any digital or cell phone camera possesses its own EV table. The main challenge in AE is to choose a proper EV regardless of the scene lighting condition. Existing AE algorithms use the relationship between Brightness Value (BV) and EV. As discussed in [5], BV is proportional to exposure duration T and is inversely proportional to the square of f-number F. Hence, one can write this relationship between EV and BVEVoptizmum=EVcurrent+log2(BVcurrent)−log2(BVoptimum)  (2)where the subscript optimum denotes the final optimum exposure under which a picture is to be taken and current denotes a current exposure. Based on Equation (2), the existing AE algorithms first take a picture with some EV, determine the BV from that picture and calculate an optimum EV using a predefined optimum BV.
Basically, different existing algorithms calculate BV and its optimum value in different ways. Mean luminance (both for green G and luminance Y channel), center-weighted mean and median luminance approaches have been widely used to serve as the optimum BV. In these algorithms, the optimum BV is considered to be the mid-level (e.g., 128 for 8-bit images). The use of mid-level brightness generates a proper exposure for scenes where the average intensity for all parts of the image is similar. However, for scenes where the average intensity for different parts of the image varies, this approach leads to getting overexposed or underexposed images. Furthermore, this one step conventional approach to reach the optimum EV is based on the assumption that there is a linear relationship between EV and BV, which does not hold under all lighting conditions.
Various statistics such as variance and entropy have been used to measure the information content of an image in a wide range of image processing applications. For example, in [6], an entropy based gamma correction technique was discussed. An entropy filter was used for sharpness or edge measurement in [7] and for line detection in [8]. Most applications using entropy have utilized it in a post processing manner with real-time operation not being a concern.
For the auto exposure application, the statistics mean luminance has been widely used due to its simplicity and low computational complexity [1-5, 9-13]. However, the mean luminance based methods fail to perform satisfactorily in all lighting conditions. More specifically, the performance of existing auto exposure methods degrades considerably in poor lighting conditions including frontlit, backlit or lowlight conditions. Accordingly, a new auto exposure method, named adaptive auto exposure, is needed to overcome the shortcomings of the existing methods.
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